RLTasksby ulam.ai
Models / Mistral

Mistral / MODEL EVIDENCE

Mistral-v0.1-7b

2 benchmarks · 9 observations · 2 sources

Scores at a glance

2 benchmarks · large dot: Mistral-v0.1-7b; small dots: closest models where reported

CRAFT-MD ↗17.4%
16.960.1

Mistral-v0.1-7b · CRAFT-MD · Free response · Vignette accuracy · combined dataset ↗

7 more reported results

7.8% · Mistral-v0.1-7b · CRAFT-MD · Free response · Multi-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗

6.2% · Mistral-v0.1-7b · CRAFT-MD · Free response · Single-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗

8.3% · Mistral-v0.1-7b · CRAFT-MD · Free response · Summarized conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗

44.1% · Mistral-v0.1-7b · CRAFT-MD · Four-choice MCQ · Vignette accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗

33.1% · Mistral-v0.1-7b · CRAFT-MD · Four-choice MCQ · Multi-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗

32.4% · Mistral-v0.1-7b · CRAFT-MD · Four-choice MCQ · Single-turn conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗

36.1% · Mistral-v0.1-7b · CRAFT-MD · Four-choice MCQ · Summarized conversation accuracy · combined dataset · An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

There are not enough shared scored settings to identify close comparisons reliably.

Filter comparisons to this model →

Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

  1. Holistic Evaluation of Language Models (HELM) ↗

    Stanford Center for Research on Foundation Models

  2. An evaluation framework for clinical use of large language models in patient interaction tasks — official CRAFT-MD leaderboard ↗

    Shreya Johri, Jaehwan Jeong, Benjamin A. Tran, Daniel I. Schlessinger, Shannon Wongvibulsin, Leandra A. Barnes et al. · 2025

    All 12 authors

    Shreya Johri, Jaehwan Jeong, Benjamin A. Tran, Daniel I. Schlessinger, Shannon Wongvibulsin, Leandra A. Barnes, Hong-Yu Zhou, Zhuo Ran Cai, Eliezer M. Van Allen, David Kim, Roxana Daneshjou, Pranav Rajpurkar

Names used in source tables

Mistral v0.1 (7B) · Mistral-v0.1-7b